Package org.apache.commons.math3.optim.nonlinear.vector

Examples of org.apache.commons.math3.optim.nonlinear.vector.Weight

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        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(1, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(2, optimum.getPoint()[1], 1e-10);
        Assert.assertEquals(3, optimum.getPoint()[2], 1e-10);
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        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(3, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(4, optimum.getPoint()[1], 1e-10);
        Assert.assertEquals(-1, optimum.getPoint()[2], 1e-10);
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        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1, 1, 1 }),
                           new InitialGuess(new double[] { 0, 0, 0 }));
    }
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        PointVectorValuePair optimum1 =
            optimizer.optimize(new MaxEval(100),
                               problem1.getModelFunction(),
                               problem1.getModelFunctionJacobian(),
                               problem1.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 1, 2, 3 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[0], 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[1], 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[2], 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[3], 1e-10);

        LinearProblem problem2 = new LinearProblem(new double[][] {
                { 10.00, 7.00, 8.10, 7.20 },
                {  7.08, 5.04, 6.00, 5.00 },
                {  8.00, 5.98, 9.89, 9.00 },
                {  6.99, 4.99, 9.00, 9.98 }
        }, new double[] { 32, 23, 33, 31 });
        PointVectorValuePair optimum2 =
            optimizer.optimize(new MaxEval(100),
                               problem2.getModelFunction(),
                               problem2.getModelFunctionJacobian(),
                               problem2.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 1, 2, 3 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(-81, optimum2.getPoint()[0], 1e-8);
        Assert.assertEquals(137, optimum2.getPoint()[1], 1e-8);
        Assert.assertEquals(-34, optimum2.getPoint()[2], 1e-8);
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        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1, 1, 1 }),
                           new InitialGuess(new double[] { 7, 6, 5, 4 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    }
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            final PointVectorValuePair optimum
                = optim.optimize(new MaxEval(Integer.MAX_VALUE),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(problem.target()),
                                 new Weight(problem.weight()),
                                 new InitialGuess(init));
            final double[] sigma = optim.computeSigma(optimum.getPoint(), 1e-14);

            // Accumulate statistics.
            for (int i = 0; i < numParams; i++) {
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        optim.optimize(new MaxEval(Integer.MAX_VALUE),
                       problem.getModelFunction(),
                       problem.getModelFunctionJacobian(),
                       new Target(t),
                       new Weight(w),
                       new InitialGuess(params));

        return optim.getChiSquare() / (t.length - params.length);
    }
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    @Test(expected=MathUnsupportedOperationException.class)
    public void testConstraintsUnsupported() {
        createOptimizer().optimize(new MaxEval(100),
                                   new Target(new double[] { 2 }),
                                   new Weight(new double[] { 1 }),
                                   new InitialGuess(new double[] { 1, 2 }),
                                   new SimpleBounds(new double[] { -10, 0 },
                                                    new double[] { 20, 30 }));
    }
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        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 problem.getTarget(),
                                 new Weight(new double[] { 1, 1, 1 }),
                                 new InitialGuess(new double[] { 0, 0, 0 }));
        Assert.assertTrue(FastMath.sqrt(optimizer.getTargetSize()) * optimizer.getRMS() > 0.6);

        optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
    }
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                                                  Precision.SAFE_MIN);
            optimizer.optimize(new MaxEval(maxCostEval),
                               problem,
                               problemJacobian,
                               new Target(new double[] { 0, 0, 0, 0, 0 }),
                               new Weight(new double[] { 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 98.680, 47.345 }));
            Assert.assertTrue(!shouldFail);
        } catch (DimensionMismatchException ee) {
            Assert.assertTrue(shouldFail);
        } catch (TooManyEvaluationsException ee) {
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Related Classes of org.apache.commons.math3.optim.nonlinear.vector.Weight

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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